It is known that the “Semantic Web” is an evolving extension of the World Wide Web in which web content can be expressed not only in natural (human) language, but also in a form that can be understood, interpreted and used by machines (e.g., computing devices) that are executing software programs (e.g., applications), thus permitting the applications to find, share and integrate information more easily. Accordingly, the growth of the Semantic Web has seen increasing amounts of knowledge in different domains being expressed using ontology languages such as the OWL Web Ontology Language (or simply “OWL”).
As is known, OWL is intended to be used when the information contained in documents needs to be processed by applications (i.e., needs to be machine-interpretable), as opposed to situations where the content only needs to be presented to humans (i.e., human-interpretable). OWL can be used to explicitly represent the meaning of terms in vocabularies and the relationships between those terms. This representation of terms and their interrelationships is referred to as an “ontology.”
Ontologies in OWL define the “concepts” (or classes), “properties” and “individuals” (or instances) relevant to some area of interest. The concepts are usually organized in a taxonomy based on a subclass relationship. Properties are associated with a domain and a range. Individuals belong to one or more concepts, and may be related to other individuals or literals through properties.
A key challenge in a number of search and information retrieval systems is finding the similarity between concepts in a taxonomy. The problem of finding the similarity between terms in a taxonomy has been widely studied. Some of these approaches use the structure of the taxonomy to derive a measure of similarity. Others make use of information-theory based approaches.
However, none of the existing approaches address the specific problem of combining taxonomic and relationship knowledge of instances (i.e., individuals) to measure their similarity.
Accordingly, improved information processing techniques are needed for measuring similarity between instances in an ontology.